Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1000
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.9 KiB
Average record size in memory136.1 B

Variable types

Text5
DateTime1
Categorical3
Boolean2
Numeric6

Alerts

ARREST is highly overall correlated with FBI CD and 1 other fieldsHigh correlation
BEAT is highly overall correlated with LATITUDE and 4 other fieldsHigh correlation
DOMESTIC is highly overall correlated with FBI CD and 1 other fieldsHigh correlation
FBI CD is highly overall correlated with ARREST and 2 other fieldsHigh correlation
LATITUDE is highly overall correlated with BEAT and 3 other fieldsHigh correlation
LONGITUDE is highly overall correlated with BEAT and 1 other fieldsHigh correlation
PRIMARY DESCRIPTION is highly overall correlated with ARREST and 2 other fieldsHigh correlation
WARD is highly overall correlated with BEAT and 2 other fieldsHigh correlation
X COORDINATE is highly overall correlated with BEAT and 3 other fieldsHigh correlation
Y COORDINATE is highly overall correlated with BEAT and 3 other fieldsHigh correlation
CASE# has unique valuesUnique

Reproduction

Analysis started2024-09-06 06:52:13.623566
Analysis finished2024-09-06 06:52:17.670575
Duration4.05 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

CASE#
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:17.849580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowJH117298
2nd rowJG561057
3rd rowJG512939
4th rowJG496628
5th rowJG512358
ValueCountFrequency (%)
jh117298 1
 
0.1%
jg512397 1
 
0.1%
jh197885 1
 
0.1%
jh164356 1
 
0.1%
jg512939 1
 
0.1%
jg496628 1
 
0.1%
jg512358 1
 
0.1%
jg496031 1
 
0.1%
jg512359 1
 
0.1%
jg444480 1
 
0.1%
Other values (990) 990
99.0%
2024-09-06T01:52:18.163660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1035
12.9%
J 1000
12.5%
5 876
10.9%
G 843
10.5%
1 674
8.4%
0 552
6.9%
2 522
6.5%
3 517
6.5%
7 511
6.4%
9 458
5.7%
Other values (3) 1012
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1035
12.9%
J 1000
12.5%
5 876
10.9%
G 843
10.5%
1 674
8.4%
0 552
6.9%
2 522
6.5%
3 517
6.5%
7 511
6.4%
9 458
5.7%
Other values (3) 1012
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1035
12.9%
J 1000
12.5%
5 876
10.9%
G 843
10.5%
1 674
8.4%
0 552
6.9%
2 522
6.5%
3 517
6.5%
7 511
6.4%
9 458
5.7%
Other values (3) 1012
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1035
12.9%
J 1000
12.5%
5 876
10.9%
G 843
10.5%
1 674
8.4%
0 552
6.9%
2 522
6.5%
3 517
6.5%
7 511
6.4%
9 458
5.7%
Other values (3) 1012
12.7%
Distinct975
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2023-08-30 09:00:00
Maximum2024-04-07 13:56:00
2024-09-06T01:52:18.286866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:18.394839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BLOCK
Text

Distinct630
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:18.604439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length23
Mean length18.622
Min length14

Characters and Unicode

Total characters18622
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)47.8%

Sample

1st row038XX W DIVERSEY AVE
2nd row004XX N WABASH AVE
3rd row056XX S ELIZABETH ST
4th row059XX N GLENWOOD AVE
5th row049XX W SCHUBERT AVE
ValueCountFrequency (%)
ave 572
 
13.9%
s 378
 
9.2%
w 320
 
7.8%
st 299
 
7.3%
n 247
 
6.0%
e 58
 
1.4%
008xx 41
 
1.0%
michigan 41
 
1.0%
dr 38
 
0.9%
pl 38
 
0.9%
Other values (481) 2082
50.6%
2024-09-06T01:52:18.911017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3114
16.7%
X 2004
 
10.8%
0 1324
 
7.1%
A 1243
 
6.7%
E 1219
 
6.5%
S 1000
 
5.4%
N 792
 
4.3%
T 720
 
3.9%
V 634
 
3.4%
W 531
 
2.9%
Other values (43) 6041
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3114
16.7%
X 2004
 
10.8%
0 1324
 
7.1%
A 1243
 
6.7%
E 1219
 
6.5%
S 1000
 
5.4%
N 792
 
4.3%
T 720
 
3.9%
V 634
 
3.4%
W 531
 
2.9%
Other values (43) 6041
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3114
16.7%
X 2004
 
10.8%
0 1324
 
7.1%
A 1243
 
6.7%
E 1219
 
6.5%
S 1000
 
5.4%
N 792
 
4.3%
T 720
 
3.9%
V 634
 
3.4%
W 531
 
2.9%
Other values (43) 6041
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3114
16.7%
X 2004
 
10.8%
0 1324
 
7.1%
A 1243
 
6.7%
E 1219
 
6.5%
S 1000
 
5.4%
N 792
 
4.3%
T 720
 
3.9%
V 634
 
3.4%
W 531
 
2.9%
Other values (43) 6041
32.4%

IUCR
Text

Distinct104
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:19.066819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)4.0%

Sample

1st row0810
2nd row0460
3rd row143A
4th row0460
5th row1320
ValueCountFrequency (%)
0486 88
 
8.8%
0820 83
 
8.3%
0910 83
 
8.3%
0810 70
 
7.0%
0460 64
 
6.4%
1320 60
 
6.0%
1310 55
 
5.5%
0560 48
 
4.8%
0920 45
 
4.5%
0890 29
 
2.9%
Other values (94) 375
37.5%
2024-09-06T01:52:19.287630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1461
36.5%
1 573
 
14.3%
2 363
 
9.1%
8 351
 
8.8%
6 301
 
7.5%
3 255
 
6.4%
4 235
 
5.9%
9 182
 
4.5%
5 156
 
3.9%
A 79
 
2.0%
Other values (3) 44
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1461
36.5%
1 573
 
14.3%
2 363
 
9.1%
8 351
 
8.8%
6 301
 
7.5%
3 255
 
6.4%
4 235
 
5.9%
9 182
 
4.5%
5 156
 
3.9%
A 79
 
2.0%
Other values (3) 44
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1461
36.5%
1 573
 
14.3%
2 363
 
9.1%
8 351
 
8.8%
6 301
 
7.5%
3 255
 
6.4%
4 235
 
5.9%
9 182
 
4.5%
5 156
 
3.9%
A 79
 
2.0%
Other values (3) 44
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1461
36.5%
1 573
 
14.3%
2 363
 
9.1%
8 351
 
8.8%
6 301
 
7.5%
3 255
 
6.4%
4 235
 
5.9%
9 182
 
4.5%
5 156
 
3.9%
A 79
 
2.0%
Other values (3) 44
 
1.1%

PRIMARY DESCRIPTION
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
THEFT
217 
BATTERY
180 
MOTOR VEHICLE THEFT
142 
CRIMINAL DAMAGE
117 
ASSAULT
89 
Other values (15)
255 

Length

Max length32
Median length26
Mean length10.762
Min length5

Characters and Unicode

Total characters10762
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowTHEFT
2nd rowBATTERY
3rd rowWEAPONS VIOLATION
4th rowBATTERY
5th rowCRIMINAL DAMAGE

Common Values

ValueCountFrequency (%)
THEFT 217
21.7%
BATTERY 180
18.0%
MOTOR VEHICLE THEFT 142
14.2%
CRIMINAL DAMAGE 117
11.7%
ASSAULT 89
8.9%
DECEPTIVE PRACTICE 47
 
4.7%
OTHER OFFENSE 45
 
4.5%
ROBBERY 40
 
4.0%
BURGLARY 34
 
3.4%
NARCOTICS 29
 
2.9%
Other values (10) 60
 
6.0%

Length

2024-09-06T01:52:19.490441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
theft 359
22.9%
battery 180
11.5%
motor 142
 
9.1%
vehicle 142
 
9.1%
criminal 137
 
8.7%
damage 117
 
7.5%
assault 95
 
6.1%
offense 49
 
3.1%
deceptive 47
 
3.0%
practice 47
 
3.0%
Other values (19) 252
16.1%

Most occurring characters

ValueCountFrequency (%)
T 1533
14.2%
E 1381
12.8%
A 935
 
8.7%
R 752
 
7.0%
I 625
 
5.8%
567
 
5.3%
H 552
 
5.1%
O 543
 
5.0%
C 496
 
4.6%
F 463
 
4.3%
Other values (14) 2915
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1533
14.2%
E 1381
12.8%
A 935
 
8.7%
R 752
 
7.0%
I 625
 
5.8%
567
 
5.3%
H 552
 
5.1%
O 543
 
5.0%
C 496
 
4.6%
F 463
 
4.3%
Other values (14) 2915
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1533
14.2%
E 1381
12.8%
A 935
 
8.7%
R 752
 
7.0%
I 625
 
5.8%
567
 
5.3%
H 552
 
5.1%
O 543
 
5.0%
C 496
 
4.6%
F 463
 
4.3%
Other values (14) 2915
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1533
14.2%
E 1381
12.8%
A 935
 
8.7%
R 752
 
7.0%
I 625
 
5.8%
567
 
5.3%
H 552
 
5.1%
O 543
 
5.0%
C 496
 
4.6%
F 463
 
4.3%
Other values (14) 2915
27.1%
Distinct97
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:19.773130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length53
Mean length16.483
Min length6

Characters and Unicode

Total characters16483
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)3.8%

Sample

1st rowOVER $500
2nd rowSIMPLE
3rd rowUNLAWFUL POSSESSION - HANDGUN
4th rowSIMPLE
5th rowTO VEHICLE
ValueCountFrequency (%)
267
 
10.1%
simple 200
 
7.6%
500 153
 
5.8%
automobile 139
 
5.2%
to 138
 
5.2%
battery 92
 
3.5%
domestic 92
 
3.5%
under 86
 
3.2%
and 86
 
3.2%
handgun 80
 
3.0%
Other values (166) 1316
49.7%
2024-09-06T01:52:20.077679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1762
 
10.7%
1649
 
10.0%
T 1329
 
8.1%
A 1070
 
6.5%
O 1027
 
6.2%
R 944
 
5.7%
I 906
 
5.5%
N 808
 
4.9%
S 696
 
4.2%
L 681
 
4.1%
Other values (28) 5611
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1762
 
10.7%
1649
 
10.0%
T 1329
 
8.1%
A 1070
 
6.5%
O 1027
 
6.2%
R 944
 
5.7%
I 906
 
5.5%
N 808
 
4.9%
S 696
 
4.2%
L 681
 
4.1%
Other values (28) 5611
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1762
 
10.7%
1649
 
10.0%
T 1329
 
8.1%
A 1070
 
6.5%
O 1027
 
6.2%
R 944
 
5.7%
I 906
 
5.5%
N 808
 
4.9%
S 696
 
4.2%
L 681
 
4.1%
Other values (28) 5611
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1762
 
10.7%
1649
 
10.0%
T 1329
 
8.1%
A 1070
 
6.5%
O 1027
 
6.2%
R 944
 
5.7%
I 906
 
5.5%
N 808
 
4.9%
S 696
 
4.2%
L 681
 
4.1%
Other values (28) 5611
34.0%
Distinct49
Distinct (%)4.9%
Missing2
Missing (%)0.2%
Memory size7.9 KiB
STREET
313 
APARTMENT
191 
RESIDENCE
105 
SIDEWALK
51 
SMALL RETAIL STORE
37 
Other values (44)
301 

Length

Max length38
Median length37
Mean length11.358717
Min length4

Characters and Unicode

Total characters11336
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.1%

Sample

1st rowSTREET
2nd rowSTREET
3rd rowRESIDENCE - YARD (FRONT / BACK)
4th rowSCHOOL - PUBLIC BUILDING
5th rowALLEY

Common Values

ValueCountFrequency (%)
STREET 313
31.3%
APARTMENT 191
19.1%
RESIDENCE 105
 
10.5%
SIDEWALK 51
 
5.1%
SMALL RETAIL STORE 37
 
3.7%
PARKING LOT / GARAGE (NON RESIDENTIAL) 34
 
3.4%
RESTAURANT 33
 
3.3%
ALLEY 29
 
2.9%
VEHICLE NON-COMMERCIAL 20
 
2.0%
HOTEL / MOTEL 18
 
1.8%
Other values (39) 167
16.7%

Length

2024-09-06T01:52:20.203411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street 313
18.6%
apartment 193
 
11.5%
168
 
10.0%
residence 141
 
8.4%
store 62
 
3.7%
sidewalk 51
 
3.0%
garage 42
 
2.5%
lot 40
 
2.4%
parking 38
 
2.3%
retail 37
 
2.2%
Other values (75) 594
35.4%

Most occurring characters

ValueCountFrequency (%)
E 1812
16.0%
T 1414
12.5%
R 1119
9.9%
A 951
 
8.4%
S 808
 
7.1%
681
 
6.0%
N 675
 
6.0%
I 528
 
4.7%
L 483
 
4.3%
O 393
 
3.5%
Other values (19) 2472
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1812
16.0%
T 1414
12.5%
R 1119
9.9%
A 951
 
8.4%
S 808
 
7.1%
681
 
6.0%
N 675
 
6.0%
I 528
 
4.7%
L 483
 
4.3%
O 393
 
3.5%
Other values (19) 2472
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1812
16.0%
T 1414
12.5%
R 1119
9.9%
A 951
 
8.4%
S 808
 
7.1%
681
 
6.0%
N 675
 
6.0%
I 528
 
4.7%
L 483
 
4.3%
O 393
 
3.5%
Other values (19) 2472
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1812
16.0%
T 1414
12.5%
R 1119
9.9%
A 951
 
8.4%
S 808
 
7.1%
681
 
6.0%
N 675
 
6.0%
I 528
 
4.7%
L 483
 
4.3%
O 393
 
3.5%
Other values (19) 2472
21.8%

ARREST
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
879 
True
121 
ValueCountFrequency (%)
False 879
87.9%
True 121
 
12.1%
2024-09-06T01:52:20.310982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

DOMESTIC
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
832 
True
168 
ValueCountFrequency (%)
False 832
83.2%
True 168
 
16.8%
2024-09-06T01:52:20.372502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

BEAT
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1196.014
Minimum111
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:20.456428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile224.95
Q1621.75
median1115
Q31832
95-th percentile2512
Maximum2535
Range2424
Interquartile range (IQR)1210.25

Descriptive statistics

Standard deviation707.42219
Coefficient of variation (CV)0.5914832
Kurtosis-0.95493039
Mean1196.014
Median Absolute Deviation (MAD)604
Skewness0.36256131
Sum1196014
Variance500446.15
MonotonicityNot monotonic
2024-09-06T01:52:20.556738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1833 27
 
2.7%
1834 25
 
2.5%
1424 19
 
1.9%
411 15
 
1.5%
331 15
 
1.5%
1223 15
 
1.5%
225 15
 
1.5%
834 14
 
1.4%
511 14
 
1.4%
2424 14
 
1.4%
Other values (237) 827
82.7%
ValueCountFrequency (%)
111 1
 
0.1%
112 1
 
0.1%
114 4
 
0.4%
121 2
 
0.2%
122 1
 
0.1%
123 13
1.3%
131 1
 
0.1%
133 1
 
0.1%
211 2
 
0.2%
212 5
 
0.5%
ValueCountFrequency (%)
2535 5
0.5%
2534 3
 
0.3%
2533 4
 
0.4%
2532 3
 
0.3%
2531 2
 
0.2%
2525 1
 
0.1%
2524 5
0.5%
2523 2
 
0.2%
2522 5
0.5%
2521 11
1.1%

WARD
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.913
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:20.656064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median22
Q334
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.196442
Coefficient of variation (CV)0.61958025
Kurtosis-1.0649226
Mean22.913
Median Absolute Deviation (MAD)12
Skewness0.19789768
Sum22913
Variance201.53897
MonotonicityNot monotonic
2024-09-06T01:52:20.758152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 58
 
5.8%
42 49
 
4.9%
20 48
 
4.8%
24 48
 
4.8%
16 44
 
4.4%
8 35
 
3.5%
49 33
 
3.3%
5 32
 
3.2%
34 32
 
3.2%
2 31
 
3.1%
Other values (40) 590
59.0%
ValueCountFrequency (%)
1 31
3.1%
2 31
3.1%
3 25
2.5%
4 13
 
1.3%
5 32
3.2%
6 25
2.5%
7 31
3.1%
8 35
3.5%
9 30
3.0%
10 11
 
1.1%
ValueCountFrequency (%)
50 12
 
1.2%
49 33
3.3%
48 12
 
1.2%
47 11
 
1.1%
46 3
 
0.3%
45 3
 
0.3%
44 15
 
1.5%
43 8
 
0.8%
42 49
4.9%
41 10
 
1.0%

FBI CD
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
06
217 
08B
157 
07
142 
14
117 
08A
57 
Other values (15)
310 

Length

Max length3
Median length2
Mean length2.279
Min length2

Characters and Unicode

Total characters2279
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row06
2nd row08B
3rd row15
4th row08B
5th row14

Common Values

ValueCountFrequency (%)
06 217
21.7%
08B 157
15.7%
07 142
14.2%
14 117
11.7%
08A 57
 
5.7%
26 52
 
5.2%
11 43
 
4.3%
04A 40
 
4.0%
03 40
 
4.0%
05 34
 
3.4%
Other values (10) 101
10.1%

Length

2024-09-06T01:52:20.852462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06 217
21.7%
08b 157
15.7%
07 142
14.2%
14 117
11.7%
08a 57
 
5.7%
26 52
 
5.2%
11 43
 
4.3%
04a 40
 
4.0%
03 40
 
4.0%
05 34
 
3.4%
Other values (10) 101
10.1%

Most occurring characters

ValueCountFrequency (%)
0 725
31.8%
6 269
 
11.8%
1 265
 
11.6%
8 243
 
10.7%
4 187
 
8.2%
B 181
 
7.9%
7 143
 
6.3%
A 98
 
4.3%
2 66
 
2.9%
5 61
 
2.7%
Other values (2) 41
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 725
31.8%
6 269
 
11.8%
1 265
 
11.6%
8 243
 
10.7%
4 187
 
8.2%
B 181
 
7.9%
7 143
 
6.3%
A 98
 
4.3%
2 66
 
2.9%
5 61
 
2.7%
Other values (2) 41
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 725
31.8%
6 269
 
11.8%
1 265
 
11.6%
8 243
 
10.7%
4 187
 
8.2%
B 181
 
7.9%
7 143
 
6.3%
A 98
 
4.3%
2 66
 
2.9%
5 61
 
2.7%
Other values (2) 41
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 725
31.8%
6 269
 
11.8%
1 265
 
11.6%
8 243
 
10.7%
4 187
 
8.2%
B 181
 
7.9%
7 143
 
6.3%
A 98
 
4.3%
2 66
 
2.9%
5 61
 
2.7%
Other values (2) 41
 
1.8%

X COORDINATE
Real number (ℝ)

HIGH CORRELATION 

Distinct635
Distinct (%)63.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1165767.3
Minimum1118625
Maximum1203237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:20.944203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1118625
5-th percentile1139256.3
Q11155497
median1165910
Q31176592
95-th percentile1191042.2
Maximum1203237
Range84612
Interquartile range (IQR)21095

Descriptive statistics

Standard deviation15108.989
Coefficient of variation (CV)0.012960554
Kurtosis-0.048889582
Mean1165767.3
Median Absolute Deviation (MAD)10682
Skewness-0.24219193
Sum1.1646015 × 109
Variance2.2828156 × 108
MonotonicityNot monotonic
2024-09-06T01:52:21.041392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1177330 26
 
2.6%
1176592 24
 
2.4%
1164309 17
 
1.7%
1175887 14
 
1.4%
1188234 11
 
1.1%
1161568 9
 
0.9%
1175253 9
 
0.9%
1165910 9
 
0.9%
1175104 9
 
0.9%
1161496 8
 
0.8%
Other values (625) 863
86.3%
ValueCountFrequency (%)
1118625 2
0.2%
1119316 1
0.1%
1119317 1
0.1%
1124413 1
0.1%
1124479 1
0.1%
1124734 2
0.2%
1127160 1
0.1%
1127389 1
0.1%
1127450 1
0.1%
1128306 1
0.1%
ValueCountFrequency (%)
1203237 1
 
0.1%
1201875 1
 
0.1%
1201126 1
 
0.1%
1199774 1
 
0.1%
1198424 1
 
0.1%
1198105 1
 
0.1%
1197905 1
 
0.1%
1197898 1
 
0.1%
1197222 4
0.4%
1197207 1
 
0.1%

Y COORDINATE
Real number (ℝ)

HIGH CORRELATION 

Distinct642
Distinct (%)64.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1889285.9
Minimum1815847
Maximum1950993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:21.157824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1815847
5-th percentile1841708.8
Q11861281.5
median1896118
Q31909321
95-th percentile1942444.6
Maximum1950993
Range135146
Interquartile range (IQR)48039.5

Descriptive statistics

Standard deviation31207.355
Coefficient of variation (CV)0.016518069
Kurtosis-0.80090875
Mean1889285.9
Median Absolute Deviation (MAD)25444
Skewness-0.035116384
Sum1.8873966 × 109
Variance9.7389901 × 108
MonotonicityNot monotonic
2024-09-06T01:52:21.268275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1906499 26
 
2.6%
1902931 24
 
2.4%
1909321 17
 
1.7%
1869033 14
 
1.4%
1855158 11
 
1.1%
1939379 9
 
0.9%
1905045 9
 
0.9%
1897037 9
 
0.9%
1949266 9
 
0.9%
1861276 8
 
0.8%
Other values (632) 863
86.3%
ValueCountFrequency (%)
1815847 1
0.1%
1817703 1
0.1%
1818013 1
0.1%
1818319 1
0.1%
1818600 1
0.1%
1818801 1
0.1%
1819156 1
0.1%
1819722 1
0.1%
1820598 1
0.1%
1822992 1
0.1%
ValueCountFrequency (%)
1950993 2
 
0.2%
1950345 3
 
0.3%
1950058 1
 
0.1%
1950021 1
 
0.1%
1950001 1
 
0.1%
1949739 3
 
0.3%
1949266 9
0.9%
1949037 1
 
0.1%
1948964 2
 
0.2%
1948934 1
 
0.1%

LATITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct643
Distinct (%)64.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean41.851775
Minimum41.649475
Maximum42.021127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-06T01:52:21.365988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41.649475
5-th percentile41.720475
Q141.775016
median41.870836
Q341.906797
95-th percentile41.997799
Maximum42.021127
Range0.37165159
Interquartile range (IQR)0.13178158

Descriptive statistics

Standard deviation0.085800548
Coefficient of variation (CV)0.0020501054
Kurtosis-0.80308217
Mean41.851775
Median Absolute Deviation (MAD)0.070040375
Skewness-0.036215674
Sum41809.923
Variance0.0073617341
MonotonicityNot monotonic
2024-09-06T01:52:21.470545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.89876792 26
 
2.6%
41.88899385 24
 
2.4%
41.9067971 17
 
1.7%
41.79599104 14
 
1.4%
41.75763099 11
 
1.1%
41.87285385 9
 
0.9%
42.01646569 9
 
0.9%
41.98924362 9
 
0.9%
41.89482493 9
 
0.9%
41.77501552 8
 
0.8%
Other values (633) 863
86.3%
ValueCountFrequency (%)
41.6494751 1
0.1%
41.65460992 1
0.1%
41.65581012 1
0.1%
41.65665763 1
0.1%
41.65740015 1
0.1%
41.65817742 1
0.1%
41.65914901 1
0.1%
41.66014234 1
0.1%
41.66316718 1
0.1%
41.66968569 1
0.1%
ValueCountFrequency (%)
42.02112669 2
 
0.2%
42.0193715 3
 
0.3%
42.01856684 1
 
0.1%
42.01852749 1
 
0.1%
42.01849501 1
 
0.1%
42.01767601 3
 
0.3%
42.01646569 9
0.9%
42.01589403 1
 
0.1%
42.01554553 2
 
0.2%
42.01548176 1
 
0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct643
Distinct (%)64.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-87.667112
Minimum-87.83907
Maximum-87.531636
Zeros0
Zeros (%)0.0%
Negative999
Negative (%)99.9%
Memory size7.9 KiB
2024-09-06T01:52:21.568483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-87.83907
5-th percentile-87.763903
Q1-87.704487
median-87.665529
Q3-87.626935
95-th percentile-87.575243
Maximum-87.531636
Range0.30743418
Interquartile range (IQR)0.07755196

Descriptive statistics

Standard deviation0.055002278
Coefficient of variation (CV)-0.00062739922
Kurtosis-0.046182812
Mean-87.667112
Median Absolute Deviation (MAD)0.038594627
Skewness-0.25663456
Sum-87579.445
Variance0.0030252505
MonotonicityNot monotonic
2024-09-06T01:52:21.666331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.62411633 26
 
2.6%
-87.62693483 24
 
2.4%
-87.67186166 17
 
1.7%
-87.63054249 14
 
1.4%
-87.58570825 11
 
1.1%
-87.63257603 9
 
0.9%
-87.68081306 9
 
0.9%
-87.66511973 9
 
0.9%
-87.63178864 9
 
0.9%
-87.68353021 8
 
0.8%
Other values (633) 863
86.3%
ValueCountFrequency (%)
-87.83907044 2
0.2%
-87.83665128 1
0.1%
-87.83658141 1
0.1%
-87.81789787 1
0.1%
-87.81762194 1
0.1%
-87.81642392 2
0.2%
-87.80758875 1
0.1%
-87.80696232 1
0.1%
-87.80660768 1
0.1%
-87.80388786 1
0.1%
ValueCountFrequency (%)
-87.53163626 1
 
0.1%
-87.53649294 1
 
0.1%
-87.53891369 1
 
0.1%
-87.54473581 1
 
0.1%
-87.54954607 1
 
0.1%
-87.55033154 1
 
0.1%
-87.55038852 1
 
0.1%
-87.55078045 1
 
0.1%
-87.55286039 4
0.4%
-87.55335704 1
 
0.1%
Distinct643
Distinct (%)64.4%
Missing1
Missing (%)0.1%
Memory size7.9 KiB
2024-09-06T01:52:21.859277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.766767
Min length26

Characters and Unicode

Total characters28738
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique496 ?
Unique (%)49.6%

Sample

1st row(41.931843966, -87.722950868)
2nd row(41.888993854, -87.626934833)
3rd row(41.791613294, -87.656024853)
4th row(41.989243623, -87.665119726)
5th row(41.929678531, -87.749824286)
ValueCountFrequency (%)
41.898767916 26
 
1.3%
87.624116333 26
 
1.3%
41.888993854 24
 
1.2%
87.626934833 24
 
1.2%
41.906797102 17
 
0.9%
87.671861659 17
 
0.9%
41.795991039 14
 
0.7%
87.630542489 14
 
0.7%
41.757630995 11
 
0.6%
87.585708249 11
 
0.6%
Other values (1276) 1814
90.8%
2024-09-06T01:52:22.156536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3166
11.0%
8 2936
10.2%
4 2534
 
8.8%
1 2532
 
8.8%
6 2348
 
8.2%
9 2014
 
7.0%
. 1998
 
7.0%
3 1626
 
5.7%
5 1618
 
5.6%
2 1594
 
5.5%
Other values (6) 6372
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 3166
11.0%
8 2936
10.2%
4 2534
 
8.8%
1 2532
 
8.8%
6 2348
 
8.2%
9 2014
 
7.0%
. 1998
 
7.0%
3 1626
 
5.7%
5 1618
 
5.6%
2 1594
 
5.5%
Other values (6) 6372
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 3166
11.0%
8 2936
10.2%
4 2534
 
8.8%
1 2532
 
8.8%
6 2348
 
8.2%
9 2014
 
7.0%
. 1998
 
7.0%
3 1626
 
5.7%
5 1618
 
5.6%
2 1594
 
5.5%
Other values (6) 6372
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 3166
11.0%
8 2936
10.2%
4 2534
 
8.8%
1 2532
 
8.8%
6 2348
 
8.2%
9 2014
 
7.0%
. 1998
 
7.0%
3 1626
 
5.7%
5 1618
 
5.6%
2 1594
 
5.5%
Other values (6) 6372
22.2%

Interactions

2024-09-06T01:52:16.756744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.182513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.744661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.269218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.725726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.198709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.831980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.296057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.816451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.342126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.806225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.276304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.907505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.428648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.957525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.414921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.882410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.352593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.982714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.510808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.031584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.486159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.956939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.432310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:17.066930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.587582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.112211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.567089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.035927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.515114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:17.149302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:14.666979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.193128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:15.648438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.119035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T01:52:16.674240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-06T01:52:22.361267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ARRESTBEATDOMESTICFBI CDLATITUDELOCATION DESCRIPTIONLONGITUDEPRIMARY DESCRIPTIONWARDX COORDINATEY COORDINATE
ARREST1.0000.0500.1030.5760.0460.1690.0450.5830.0150.0480.050
BEAT0.0501.0000.1580.0780.7770.255-0.5330.0750.642-0.5400.776
DOMESTIC0.1030.1581.0000.5420.1210.3970.0890.5260.1530.0750.143
FBI CD0.5760.0780.5421.0000.0710.1540.0580.8890.0600.0670.070
LATITUDE0.0460.7770.1210.0711.0000.212-0.4960.0460.674-0.5081.000
LOCATION DESCRIPTION0.1690.2550.3970.1540.2121.0000.1630.1580.2400.1640.214
LONGITUDE0.045-0.5330.0890.058-0.4960.1631.0000.061-0.4681.000-0.493
PRIMARY DESCRIPTION0.5830.0750.5260.8890.0460.1580.0611.0000.0650.0740.046
WARD0.0150.6420.1530.0600.6740.240-0.4680.0651.000-0.4760.673
X COORDINATE0.048-0.5400.0750.067-0.5080.1641.0000.074-0.4761.000-0.505
Y COORDINATE0.0500.7760.1430.0701.0000.214-0.4930.0460.673-0.5051.000

Missing values

2024-09-06T01:52:17.271436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-06T01:52:17.465918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-06T01:52:17.603345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CASE#DATE OF OCCURRENCEBLOCKIUCRPRIMARY DESCRIPTIONSECONDARY DESCRIPTIONLOCATION DESCRIPTIONARRESTDOMESTICBEATWARDFBI CDX COORDINATEY COORDINATELATITUDELONGITUDELOCATION
0JH11729801/16/2024 01:00:00 AM038XX W DIVERSEY AVE0810THEFTOVER $500STREETNN252435061150337.01918345.041.931844-87.722951(41.931843966, -87.722950868)
1JG56105712/31/2023 04:30:00 PM004XX N WABASH AVE0460BATTERYSIMPLESTREETNN18344208B1176592.01902931.041.888994-87.626935(41.888993854, -87.626934833)
2JG51293911/21/2023 02:28:00 PM056XX S ELIZABETH ST143AWEAPONS VIOLATIONUNLAWFUL POSSESSION - HANDGUNRESIDENCE - YARD (FRONT / BACK)NN71316151168951.01867382.041.791613-87.656025(41.791613294, -87.656024853)
3JG49662811/08/2023 03:27:00 PM059XX N GLENWOOD AVE0460BATTERYSIMPLESCHOOL - PUBLIC BUILDINGYN20134808B1165910.01939379.041.989244-87.665120(41.989243623, -87.665119726)
4JG51235811/21/2023 02:12:00 AM049XX W SCHUBERT AVE1320CRIMINAL DAMAGETO VEHICLEALLEYNN252131141143030.01917505.041.929679-87.749824(41.929678531, -87.749824286)
5JG49603111/08/2023 07:00:00 AM075XX S STONY ISLAND AVE0460BATTERYSIMPLEHOSPITAL BUILDING / GROUNDSNN411808B1188234.01855158.041.757631-87.585708(41.757630995, -87.585708249)
6JG51235911/21/2023 01:38:00 AM002XX W 37TH PL0560ASSAULTSIMPLEAPARTMENTYY915308A1175127.01880095.041.826363-87.632999(41.826363218, -87.632998863)
7JG44448009/30/2023 01:50:00 AM034XX W FLOURNOY ST0486BATTERYDOMESTIC BATTERY SIMPLEVEHICLE NON-COMMERCIALNY11332408B1153640.01896821.041.872715-87.711386(41.872714939, -87.711386229)
8JG51937211/17/2023 04:30:00 PM004XX N PINE AVE031AROBBERYARMED - HANDGUNSIDEWALKNN152337031139464.01902305.041.888034-87.763300(41.888033817, -87.763299736)
9JG48323710/28/2023 07:30:00 PM068XX S DR MARTIN LUTHER KING JR DR0910MOTOR VEHICLE THEFTAUTOMOBILEVEHICLE NON-COMMERCIALNN3226071180091.01860009.041.771133-87.615403(41.771132967, -87.615402602)
CASE#DATE OF OCCURRENCEBLOCKIUCRPRIMARY DESCRIPTIONSECONDARY DESCRIPTIONLOCATION DESCRIPTIONARRESTDOMESTICBEATWARDFBI CDX COORDINATEY COORDINATELATITUDELONGITUDELOCATION
990JG55941612/30/2023 05:40:00 AM043XX N MILWAUKEE AVE1310CRIMINAL DAMAGETO PROPERTYPARKING LOT / GARAGE (NON RESIDENTIAL)NN162445141141808.01928592.041.960125-87.754040(41.960125037, -87.754039508)
991JG55937412/30/2023 03:58:00 AM009XX W ADDISON ST0460BATTERYSIMPLERESTAURANTNN19234408B1169198.01924103.041.947255-87.653472(41.947254789, -87.653471904)
992JG56089912/30/2023 09:57:00 PM110XX S PARNELL AVE0810THEFTOVER $500DRIVEWAY - RESIDENTIALNN223321061174599.01831720.041.693628-87.636374(41.69362823, -87.63637388)
993JG55966312/30/2023 11:41:00 AM013XX N MILWAUKEE AVE0860THEFTRETAIL THEFTDEPARTMENT STORENN14241061164309.01909321.041.906797-87.671862(41.906797102, -87.671861659)
994JH11155601/09/2024 11:10:00 PM035XX S HALSTED ST2093NARCOTICSFOUND SUSPECT NARCOTICSOTHER (SPECIFY)NN91511181171544.01881570.041.830490-87.646101(41.830490151, -87.646100807)
995JH14158802/05/2024 09:03:00 PM061XX N HOYNE AVE0486BATTERYDOMESTIC BATTERY SIMPLEPARKING LOT / GARAGE (NON RESIDENTIAL)NY24134008B1161209.01940913.041.993552-87.682368(41.99355232, -87.682367785)
996JH17855703/07/2024 04:45:00 AM026XX W ARMITAGE AVE041ABATTERYAGGRAVATED - HANDGUNSTREETNN1421104B1158387.01913184.041.917521-87.693510(41.917520732, -87.693509757)
997JH14085802/05/2024 08:30:00 AM013XX N LA SALLE DR0810THEFTOVER $500STREETNN18212061174889.01909203.041.906243-87.633001(41.906242849, -87.633000828)
998JH14040602/05/2024 03:29:00 AM007XX S CLARK ST0486BATTERYDOMESTIC BATTERY SIMPLEAPARTMENTYY123408B1175633.01897092.041.872993-87.630632(41.872992906, -87.63063219)
999JH14015802/04/2024 07:15:00 PM007XX W IRVING PARK RD502POTHER OFFENSEFALSE / STOLEN / ALTERED TRPSTREETYN191546261170563.01926785.041.954584-87.648376(41.954584472, -87.648375776)